End-to-end deep learning model for detecting fetal QRS complexes directly from non-invasive abdominal ECG (aECG) signals, without requiring a separate reference maternal ECG or heavy hand-crafted feature engineering. The model adopts a ResNet architecture built from 1-D octave convolutions (OctConv), which factorize feature maps into high- and low-frequency components to capture multiple temporal frequency scales while reducing memory and compute cost relative to a standard 1-D ResNet; the resulting feature-importance weighting also highlights the signal regions most relevant to each detection. Evaluated on the PhysioNet/CinC Challenge 2013 fetal ECG database (with added Gaussian and motion-artifact noise to mimic real-world recording conditions), the model reached an F1 score of 91.1% while cutting computation by more than 50% for less than a 2% drop in performance versus a non-octave ResNet baseline.